# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import inspect
import unittest

import numpy as np
from op_test import OpTest

import paddle
from paddle import base
from paddle.base import core


class TestDiagEmbedOp(OpTest):
    def setUp(self):
        self.op_type = "diag_embed"
        self.python_api = paddle.diag_embed
        self.init_shape()
        self.init_config()
        self.outputs = {'Out': self.target}

    def test_check_output(self):
        self.check_output(check_pir=True)

    def init_shape(self):
        self.shape = (2, 3)

    def init_config(self):
        self.case = np.random.randn(*self.shape).astype('float32')
        self.inputs = {'Input': self.case}
        self.attrs = {'offset': 0, 'dim1': -2, 'dim2': -1}
        self.target = np.stack([np.diag(r, 0) for r in self.inputs['Input']], 0)


class TestDiagEmbedOpCase1(TestDiagEmbedOp):
    def init_config(self):
        self.case = np.random.randn(2, 3).astype('float32')
        self.inputs = {'Input': self.case}
        self.attrs = {'offset': -1, 'dim1': 0, 'dim2': 2}
        self.target = np.stack(
            [np.diag(r, -1) for r in self.inputs['Input']], 1
        )


class TestDiagEmbedOp_ZeroSize(TestDiagEmbedOp):
    def init_shape(self):
        self.shape = (2, 0)


class TestDiagEmbedAPICase(unittest.TestCase):
    def test_case1(self):
        paddle.enable_static()
        main = paddle.static.Program()
        startup = paddle.static.Program()
        with paddle.static.program_guard(main, startup):
            diag_embed = np.random.randn(2, 3, 4).astype('float32')
            data1 = paddle.static.data(
                name='data1', shape=[2, 3, 4], dtype='float32'
            )
            out1 = paddle.diag_embed(data1)
            out2 = paddle.diag_embed(data1, offset=1, dim1=-2, dim2=3)

            place = core.CPUPlace()
            exe = base.Executor(place)
            results = exe.run(
                main,
                feed={"data1": diag_embed},
                fetch_list=[out1, out2],
                return_numpy=True,
            )
            target1 = np.stack(
                [np.stack([np.diag(s, 0) for s in r], 0) for r in diag_embed], 0
            )
            target2 = np.stack(
                [np.stack([np.diag(s, 1) for s in r], 0) for r in diag_embed], 0
            )
            np.testing.assert_allclose(results[0], target1, rtol=1e-05)
            np.testing.assert_allclose(results[1], target2, rtol=1e-05)

    def test_tensor_method(self):
        paddle.disable_static()
        x = paddle.arange(15).reshape((3, 5)).astype('float64')
        self.assertTrue(inspect.ismethod(x.diag_embed))


if __name__ == "__main__":
    unittest.main()
